Case Study. Oates Lab

School of Life Sciences
Lausanne, Switzerland
How do spatio-temporal patterns emerge at the tissue level from noisy cellular and molecular interactions? What are the principles that govern transitions from parts to wholes, and those that determine precision and robustness?

Oates Lab explores these issues using a population of genetic oscillators in the vertebrate embryo termed the segmentation clock. This multi-cellular clock drives the rhythmic, sequential, and precise formation of embryonic body segments, exhibiting rich spatial and temporal phenomena spanning from molecular to tissue scales. Tissue patterning by cellular oscillations is a recent concept, and the mechanisms and molecules responsible for this astonishing activity are just beginning to be understood.

Fluorescence live imaging is used throughout many projects in the lab to follow the behaviors of hundreds  of cells in parallel during the development of the embryo. After image acquisition, the position of each cell is determined at each timepoint, and the positions are connected across the timepoints. This allows to study how cells move inside the embryo and how they interact with each other to form tissue and ultimately a full embryo.

Research Example

In a recent project in Andrew Oates laboratory (Rohde et al., 2021), they were able to track cells over 7.5 hours in a freely-growing embryo using nuclear marker and a fluorescently-tagged protein, Her1, used to define the tissue-level pattern.

The study compares the cellular-resolution oscillatory pattern in the embryo with those generated in individual cells in culture to understand and therefore propose a mechanism which can explain the observed pattern.
15 somite stage zebrafish embryo; max projection of the histone marker (in grey) and the a segmentation clock marker, Her1 (in magenta). Credit: Arianne Bercowsky, Oates Lab, EPFL


Volumetric live cell imaging requires high-imaging speed acquisition with the least photo dose possible to reduce phototoxicity.

Light-sheet microscopy has established itself as the technique of choice for high speed 3D imaging thanks to its gentle illumination and imaging speed. However, compared to more established fluorescence widefield or confocal, light-sheet microscopy faces more complicated sample mounting that limits the possibility to run multi-positions imaging experiments.
Moreover, developing zebrafish embryos undergo morphological transformations that lead to the sample moving inside the sample chamber making it difficult to be able to image a region of interest over time.

Another limitation is that the fluorescence reporters are expressed at a very low level inside the fish. So the user has to find a compromise between imaging speed (exposure time of the camera) and the amount of fluorescence signal collected to be able to follow the biological process.
Oates Lab data factsheet
Size of a single image saved as 3D Stack (XYZ)
Size of a full time-lapse movie dataset
6 movies
Number of datasets acquired per week
Transfer time on 1Gbit connection (125MB/s)

Storage setup:

2 short-term on-premise servers with a capacity of 260 TB, and 1 long-term storage system provided by EPFL in Amazon S3 buckets

Storage process and duration:

On-premise processing servers. After publication, the archive is moved to a 10 years long-term storage with access via standard S3 (AWS) protocol. The rest of the data is stored as long as the project lasts, from 5 to 15 years.
Large image data causes challenges beyond image acquisition. Storage space, associated costs, speed of transfer and processing, and the data quality.

The laboratory of Andrew Oates currently generates about 913 GB of raw data per 1 movie, and about 6 movies per week.  If the acquisition rate remains the same for a year, then the data volume would account for 270 TB a year.

Such volume poses serious challenges.
First, the data is transferred through a costly high quality network to a short-term storage system. Transfer and processing of such data takes a substantial time, delaying the research.
Second, the data storage costs are growing exponentially. For example, a centralised workstation for image processing, GPU-computing and big data storage (130 TBs) costs $120,000. With electricity, cooling system, and IT staff maintenance the costs grow substantially.

Third, large data takes up storage space quickly. The simple solutions are either buying a new system or deleting the data to free up space, which opposes industry recommendation for re-using the data. The less data is re-used, the more animals are used. So the most reasonable solution is data compression. However, typical lossless algorithms offer low ratios, while lossy compression doesn’t preserve full information, thus affecting the quality of the data.


To overcome the above mentioned challenges, Oates Lab set up an imaging and data analysis pipeline that relies on the combination of three key technological features:
Viventis LS1 Live light sheet microscope with intelligent imaging
Hamamatsu high-end sCMOS back-thinned camera ORCA-Fusion BT
Jetraw image compression software


The LS1 Live is a complete light sheet system for live imaging that combines the advantages of low phototoxicity and high imaging speed with the ability to run multi-positions experiment and easy sample mounting.


The ORCA-Fusion BT camera is the pinnacle of scientific CMOS (sCMOS) performance. The specifications are without compromise: ultra-low readout noise, CCD-like uniformity, fast frame rates and back-thinned enabled high QE.


Jetraw is a unique metrologically accurate raw image compression software. It delivers best in class performance, combining the benefits of both lossy and lossless formats. It offers unparalled 7:1 ratio at 200MB/sec/core processing speed, while retaining full image information.
The Viventis LS1 Live light sheet microscope has an inverted configuration and an open top sample chamber that allow the mounting of multiple fish in parallel without the need for agarose embedding. This allows the user to easily position the fish in the sample chamber and the fish embryo to develop unconstrained during imaging. Moreover, a high NA detection lens with a large field of view (FOV) allows an efficient collection of the emitted fluorescence light from the sample while being able to image the entire whole early embryo (Figure 1).

The use of Hamamatsu’s ORCA-Fusion BT camera in this setup maximises the overall detection efficiency. It has a combination of high speed and large field of view (FOV) which enables the lightsheet setup to generate rapid 3D information. The high QE and low noise ensures high SNR images at the desired short exposure times, whilst keeping the excitation intensity low to avoid any phototoxicity to the sensitive developing embryo.
Figure 1:  Early fish embryo development; grey: nuclei, yellow: cycling gene. Credit: Olivier Venzin, Oates Lab, EPFL
Figure 2: Early fish embryo development; grey: Actin. Movie credit: Sundar Naganathan, Oates Lab, EPFL
Viventis microscope also adapts its imaging parameters based on the sample. For example, imaging the tail of a fish is difficult as it grows and extends larger than the FOV of the microscope. The microscope in use is able to track the tail while it grows by adapting the coordinates of its mechanical sample stage in an automatic way. This enables imaging at high single cell resolution of a region of interest of the tail over long term. (Figure 2).
Jetraw software compression integrated into Viventis Microscopy System allows Andrew Oates Laboratory to acquire images 4x faster (previous compression systems were slower) with impressive compression ratio (1:8) without losing any information useful for the later analysis.
With Jetraw, the lab immediately started benefitting in terms of data transfer speed, pstorage space, the costs (they pay per TB/year), and the environmental impact.

Jetraw solution also allows the lab to work on more movies simultaneously, as they have more local and cloud space.


1.57GB → 196.2MB

Size of a single image saved as 3D Stack (XYZ)

913GB → 115GB

Size of a full time-lapse movie dataset

2h → 15.2min

Transfer time on 1Gbit connection (125MB/s)

Jetraw Impact

$1.35M → 0.2M

Data generation of 270TB/year data, with a project running for 5 years and data stored for 10 years, would incur in $1,350,000 bill. In contrast, with Jetraw and the rest of the parameters intact, the cost would be reduced to $168,750, saving the lab over $1M per project running for 5 years, which they could invest in the research, equipment, and the staff.

431t → 39t of CO2

270 TB/year acquisition and a required archival over 10 years would incur in 431 tonnes of CO2 emission, per a set of data without a backup. With Jetraw carbon footprint is reduced to 39 tonnes. Saving of almost 275 tonnes is equivalent to planting 12,400 trees to grow for a year, which is a size of about 11 football pitches. If the lab was to commercially offset such a volume of emissions with one of the offset/tree planting services in Europe, it would cost around €17,400 per project w/o a backup

Focus on discoveries

Due to Jetraw compression, Oates Lab can now store more images, which allows them to re-use the data, meaning they utilize less animals in their research, and they could have larger sample data for more accurate research. Typical data transfer now takes just 15 minutes instead on 2 hours, saving scientists so much more valuable time they could instead invest into actual research and analysis.

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Pomarico, E., Schmidt, C., Chays, F. et al. Statistical distortion of supervised learning predictions in optical microscopy induced by image compression. Sci. Rep. 12, 3464 (2022)

Rohde, L.A, Bercowsky-Rama, A., Negrete, J. Jr., et al. Cell-autonomous generation of the wave pattern within the vertebrate segmentation clock. Preprint at bioRxiv (2021)

Naganathan, S.R., Popović, M. & Oates, A.C. Left–right symmetry of zebrafish embryos requires somite surface tension. Nature 605, 516–521 (2022)

Venzin, O. F. & Oates, A. C. What are you synching about? Emerging complexity of Notch signaling in the segmentation clock. Dev. Biol. 460, 40-54 (2020)